(1) Genotype-Tissue Expression (GTEx) Largest systematic study of genetic regulation in multiple tissues to date 53 tissues, 500+ donors, 9K samples, 180M gene RNA-Seq, 14M eQTL, 2M+ gene interactions, 30K+ gene annotations Data Donor Age, Gender, Death Type Tissue RNA-Seq expression values Gene Tissue Genetic SNP associations Interaction partners Biological processes, Molecular functions, Diseases Tissue Hierarchy Mele et al Science 2015, Ardi et al Science 2015

(1) GTEx: Personal transcriptomics Many key challenges in medicine involve tissue specificity Generic tasks: Find top 100 active interaction modules in a tissue Find informative tissue features Specialized tasks: Predict tissue-specific processes Predict gene-disease associations Predict cross-tissue interactions Predict tissue-specific interactions Mele et al Science 2015

(2) Cancer Genome Consortium (ICGC) International data collection of cancer data: 60+ cancer types, 18K+ donors, 37M+ mutations Data Cancer type Donor Tumor stage, Vital status, Disease status, Age, Relapse, Gender Healthy samples: Microarray expressions, RNA-seq expressions, Mutations, Methylation, Copy-number variation Tumor samples: Microarray expressions, RNA-seq expressions, Mutations, Methylation, Copy-number variation Gene-gene interactions Lawrence et al Nature 2013, Creixell et al. Nature Methods 2015

(2) Cancer Genome Consortium (ICGC) Goal: Integrate multiple layers of information across all cancers Tasks: Predict patients’ survival time Identify cancer subtypes with clinically relevant differences Identify abrupt changes between tumor stages progression -> remission -> relapse Relevant papers: Network-based stratification of tumor mutations, M. Hofree et al., Nature Methods 2013 Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes, MDM Leiserson et al., Nature Genetics 2015 Pathway and network analysis of cancer genomes, P. Creixell et al., Nature Methods 2015